Update README.md

README.md

@@ -1,22 +1,22 @@
 ---
 license: apache-2.0
+datasets:
+- XavierJiezou/cloud-adapter-datasets
+language:
+- en
+metrics:
+- mean_iou
+base_model:
+- facebook/dinov2-large
 ---
 
 
 # Cloud Adapter Models
 
-This repository contains the code and pre-trained model weights for the paper **"Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images"**. The models are specifically designed to perform robust cloud segmentation in remote sensing imagery by leveraging and fine-tuning vision foundation models.
-
-## Features
-
-- Pre-trained model weights for cloud segmentation tasks.
-- Code for fine-tuning and evaluation of the models on remote sensing datasets.
-- A user-friendly **Gradio Demo** to test the models interactively.
+This repository contains the pre-trained model weights for the [Cloud-Adapter](https://xavierjiezou.github.io/Cloud-Adapter/).
 
 ## Installation
 
-To use the code in this repository, clone it locally and install the required dependencies:
-
 ```bash
 git clone https://huggingface.co/XavierJiezou/cloud-adapter-models
 cd cloud-adapter-models
@@ -25,46 +25,22 @@ pip install -r requirements.txt
 
 ## Usage
 
-### 1. Download Pre-trained Models
-
-The pre-trained model weights are available in the repository. Download the weights and place them in the appropriate directory.
-
-
-
-### 2. Run the Gradio Demo
-
-To interactively test the models using Gradio:
-
 ```bash
 python app.py
 ```
 
-#### Notes:
-- **GPU Requirement**: If using a GPU, ensure it has at least **16GB of VRAM** to run the model efficiently.
-- **CPU-Only Mode**: If you wish to run the demo on CPU, set the environment variable `CUDA_VISIBLE_DEVICES` to `-1`:
-
-```bash
-CUDA_VISIBLE_DEVICES=-1 python app.py
-``` 
-
-
-This will launch a web interface where you can upload remote sensing images and view the segmentation results.The Gradio demo allows users to upload remote sensing images, run cloud segmentation, and visualize the results. It can be easily modified to suit custom datasets or tasks.
-
-
 ## Citation
 
-If you find this repository helpful, please consider citing the paper:
+If you use our code or models in your research, please cite with:
 
 ```latex
-@{cloud-adapter,
-title={Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images}, 
-author={Xuechao Zou and Shun Zhang and Kai Li and Shiying Wang and Junliang Xing and Lei Jin and Congyan Lang and Pin Tao},
-year={2024},
-eprint={2411.13127},
-archivePrefix={arXiv},
-primaryClass={cs.CV},
-url={https://arxiv.org/abs/2411.13127}
+@misc{zou2024adaptingvisionfoundationmodels,
+      title={Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images}, 
+      author={Xuechao Zou and Shun Zhang and Kai Li and Shiying Wang and Junliang Xing and Lei Jin and Congyan Lang and Pin Tao},
+      year={2024},
+      eprint={2411.13127},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2411.13127}, 
 }
-```
-
-
+```